arXiv AI
CoASFL: Coupled Consistency Optimization for Asynchronous Split Federated Learning
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GAPSL: A Gradient-Aligned Parallel Split Learning over Data-Heterogeneous Edge Computing Systems
arXiv:2603. 18540v2 Announce Type: replace Abstract: The increasing complexity of neural networks poses significant challenges for democratizing federated learning (FL) on resource-constrained edge devices.
arXiv Machine Learning
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Ampere: Communication-Efficient and High-Accuracy Split Federated Learning
Ampere is a new split federated learning system that reduces both on‑device computation and device‑server communication while improving accuracy. It trains device and server blocks sequentially with local losses, eliminating gradient transfers, and uses a lightweight auxiliary network to consolidate activations into a single transfer. Experiments on CNNs and Transformers show up to 11.70 pp accuracy gains, 18.6× faster training, 911× less communication, and 14.5× less computation compared to state‑of‑the‑art SFL baselines.
arXiv Machine Learning
Aug 5
AS-FedBridge: Pseudo-Spike Bridge Distillation for Heterogeneous ANN-SNN Federated Learning
arXiv:2608. 03324v1 Announce Type: new Abstract: Federated learning enables collaborative model training across distributed edge devices while strictly preserving data privacy.
arXiv AI
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When Is Shallow Enough? Adaptive Split Federated Learning with Client-Specific Sufficiency Estimation
arXiv:2608. 15639v1 Announce Type: cross Abstract: \textit{Split Federated Learning} (SFL) enables distributed model training by splitting networks between the server and clients.
Hugging Face Trending Papers
Aug 16
When Is Shallow Enough? Adaptive Split Federated Learning with Client-Specific Sufficiency Estimation
\textit{Split Federated Learning} (SFL) enables distributed model training by splitting networks between the server and clients. However, under client heterogeneity, the conventional static split stra...
arXiv Machine Learning
Oct 3
FedSAP: Federated Learning with Structured Adaptive Partitioning for Multi-Domain Heterogeneous Edge Devices
FedSAP is a federated learning framework that addresses heterogeneous edge devices by using structured pruning as a budget-constrained tri-state channel allocation. It partitions model channels into a Global pool, pseudo-domain-specific Private pools, and a Dropped state, allowing broadly useful features to be shared while isolating domain-sensitive updates. Experiments on Digits and Office-Caltech datasets show FedSAP achieving higher mean global accuracy than the strongest baseline while supporting up to 80% client pruning ratios.